5 citations · 7 across the 11 of their papers we have counts for
12 papers
PinchBot: Long-Horizon Deformable Manipulation with Guided Diffusion Policy
Alison Bartsch, Arvind Car, Amir Barati Farimani
Pottery creation is a complicated art form that requires dexterous, precise and delicate actions to slowly morph a block of clay to a meaningful, and often useful 3D goal shape. In…
RT-Cache: Training-Free Retrieval for Real-Time Manipulation
Owen Kwon, Abraham George, Alison Bartsch +1
Real robots are expected to repeat the same behavior in new environments with very little new data, yet modern controllers either incur heavy per-step inference or require deployme…
Planning and Reasoning with 3D Deformable Objects for Hierarchical Text-to-3D Robotic Shaping
Alison Bartsch, Amir Barati Farimani
Deformable object manipulation remains a key challenge in developing autonomous robotic systems that can be successfully deployed in real-world scenarios. In this work, we explore…
PLATO: Planning with LLMs and Affordances for Tool Manipulation
Arvind Car, Sai Sravan Yarlagadda, Alison Bartsch +2
As robotic systems become increasingly integrated into complex real-world environments, there is a growing need for approaches that enable robots to understand and act upon natural…
LLM-Craft: Robotic Crafting of Elasto-Plastic Objects with Large Language Models
Alison Bartsch, Amir Barati Farimani
When humans create sculptures, we are able to reason about how geometrically we need to alter the clay state to reach our target goal. We are not computing point-wise similarity me…
SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy
Alison Bartsch, Arvind Car, Charlotte Avra +1
Manipulating deformable objects remains a challenge within robotics due to the difficulties of state estimation, long-horizon planning, and predicting how the object will deform gi…
SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation
Alison Bartsch, Charlotte Avra, Amir Barati Farimani
Deformable object manipulation presents a unique set of challenges in robotic manipulation by exhibiting high degrees of freedom and severe self-occlusion. State representation for…
Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction
Jong Hoon Park, Gauri Pramod Dalwankar, Alison Bartsch +2
Accurately determining fluid viscosity is crucial for various industrial and scientific applications. Traditional methods of viscosity measurement, though reliable, often require m…
RoboChop: Autonomous Framework for Fruit and Vegetable Chopping Leveraging Foundational Models
Atharva Dikshit, Alison Bartsch, Abraham George +1
With the goal of developing fully autonomous cooking robots, developing robust systems that can chop a wide variety of objects is important. Existing approaches focus primarily on…
OpenVR: Teleoperation for Manipulation
Abraham George, Alison Bartsch, Amir Barati Farimani
Across the robotics field, quality demonstrations are an integral part of many control pipelines. However, collecting high-quality demonstration trajectories remains time-consuming…
Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning
Abraham George, Alison Bartsch, Amir Barati Farimani
The use of human demonstrations in reinforcement learning has proven to significantly improve agent performance. However, any requirement for a human to manually 'teach' the model…
MAN: Multi-Action Networks Learning
Keqin Wang, Alison Bartsch, Amir Barati Farimani
Learning control policies with large discrete action spaces is a challenging problem in the field of reinforcement learning due to present inefficiencies in exploration. With high…